Checkout lanes at retail store locations like grocery stores present risk of loss for retailers during each customer checkout process at those retail stores. Shopping carts are typically designed to comprise a basket and a lower tray. The lower tray is often used for large items such as laundry detergent, boxed canned beverages, packaged sets of paper towels, and/or bags of pet food. Relatedly, items placed on the lower tray of a shopping cart often have a higher than average price per unit compared to items that are typically placed in the basket of the shopping cart.
Currently, retail store clerks operating a checkout lane are limited in their tools to evaluate shopping carts entering into a checkout lane. Clerks (or cashiers) often have easy visibility into the basket itself; however, clerks have less visibility into whether there are items on the lower tray of the shopping cart. While baskets to shopping carts can have wire framing that permit a person to see through to the lower tray, that view to the lower tray becomes obstructed by objects in the basket from a top-down view. Additionally, clerks are often stationary at the checkout lane's register without an opportunity to move around the checkout lane to establish a more direct line of sight with the lower tray to check for any items on the lower tray.
This limitation on the clerk's ability to move for a more direct line of sight can result from a variety of reasons, including a need to checkout items as quickly as possible due to high volumes of customers and/or a need or pressure to not imply a distrust of the customers checking out through the checkout lane. As a result, retail store locations can, and often do, experience losses from unidentified items passing through checkout lanes on the lower trays of shopping carts, whether due to customers failing to remember items are on the lower trays or customers intentionally avoiding payment obligations for such items.
Existing solutions for detection of objects on the lower tray of a shopping cart suffer from shortcomings that limit their usefulness and actual use in the field. Several solutions suffer from being impractically expensive due to the requirement of purchasing expensive cameras, additional lighting, additional monitors, and mounting equipment for the same, e.g., to enable a cashier to view a live-video stream of the lower tray of shopping carts passing through the cashier's checkout lane. Other solutions require high contrast materials secured to the checkout lane aisle flooring and/or lower tray of the shopping carts to enable the solutions' respective analytical processes to accurately perform image data comparisons and make a determination whether one or more items are located on the lower tray of a shopping cart. Still other solutions are burdened by the sheer diversity of point-of-sale (POS) systems across retail stores, even under the same retailer banner. For example, in some embodiments, other solutions suffer from being required to integrate with the POS system for a retail store location and implementation of the solution requires additional, costly and sometimes unsuccessful customization of the solution, which is problematic across tens, hundreds or thousands of stores. What's more, integrating with a POS system presents significant security concerns that retail stores want to avoid and minimize (i.e., from having a third party connect to the system responsible for processing personal financial information from payments). Relatedly, integrating and interfacing a solution with a retail store's location can present the alternative or additional problem of interfering with the checkout process. For example, some solutions that specifically attempt to identify the product that is on the lower tray of a shopping cart may incorrectly identify that product and a customer may be charged an incorrect amount, which affects customer satisfaction as well as may cause errors in a retail store location's inventory management if the error is left uncorrected. Alternatively, solutions that specifically attempt to identify the product that is on the lower tray of a shopping cart may fail to identify the product (e.g., because of a lack of training data for the product) and the solutions fail to indicate an item is on the lower tray of the shopping cart, thereby defeating the purpose of the solution as a bottom-of-basket detection system. As another example, in some embodiments, solutions suffer from requiring specific training images of individual products and their packaging in order to actually identify such products are located on the lower tray of a shopping cart. Given the volume of products and even the variety of packaging designs, sizes, and possible orientations of products for a single brand, this creates persistent retraining needs that may hamper system operations and outcomes, particularly if such a solution is also integrated with a POS system.